Assisted driving and autonomous driving technologies rely on control signals provided to vehicles by algorithms that process data collected by a variety of different sensors carried by those vehicles. Many of these algorithms, such as computer vision and sensor fusion algorithms, are developed by machine learning. Development of algorithms by machine learning requires large amounts of ground truth data for training and evaluation.
Sensors can be used to collect data, which may then be used to synthetically generate environments simulating the environments from which the sensors collect the data. The simulated environments can provide accurate datasets with which to develop algorithms for autonomous or assisted driving by machine learning. However, there presently are no methods for providing simulated environments that can provide datasets that can train and/or evaluate algorithms that would make use of radar systems and/or ultrasonic systems.